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Nguyen, Nhung T. T.,Lo, Tien N. H.,Kim, Jaheon,Nguyen, Huong T. D.,Le, Toan B.,Cordova, Kyle E.,Furukawa, Hiroyasu American Chemical Society 2016 Inorganic Chemistry Vol.55 No.12
<P>A presynthesized, square planar copper imidazole complex, [Cu(imidazole)(4)](NO3)(2), was utilized as a precursor in the synthesis of a new series of zeolitic imidazolate frameworks, termed ZIF-202, -203, and -204. The structures of all three members were solved by single-crystal X-ray diffraction analysis, which revealed ZIF-203 and -204 having successfully integrated square planar units within the backbones of their respective frameworks. As a result of this unit, the structures of both ZIF-203 and -204 were found to adopt unprecedented three-dimensional nets, namely, ntn and thl, respectively. One member of this series, ZIF-204, was demonstrated to be highly porous, exhibit exceptional stability in water, and selectively capture CO2 over CH4 under both dry and wet conditions without any loss in performance over three cycles. Remarkably, the regeneration of ZIF-204 was performed under the mild conditions of flowing a pure N-2 gas through the material at ambient temperature.</P>
모바일 시스템 응용을 위한 실외 한국어 간판 영상에서 텍스트 검출 및 인식
박종현(J.H. Park),이귀상(G.S. Lee),김수형(S.H. Kim),이명훈(M.H. Lee),Nguyen Dinh Toan(N.D. Toan) 대한전자공학회 2009 電子工學會論文誌-CI (Computer and Information) Vol.46 No.2
자연 영상에서의 텍스트 이해는 지난 수년간 매우 활발한 연구 분야로 자리하고 있다. 논문에서 우리는 한국어 간판영상으로부터 자동으로 텍스트를 인식하는 방법을 제안한다. 제안된 방법은 상호명의 인식을 위한 텍스트 영역의 검출 및 이진화를 포함하고 있다. 먼저 수직, 수평 방향의 에지 히스토그램을 이용하여 텍스트 영역의 정교한 검출을 수행 하였다. 두 번째 단계는 검출된 텍스트 영역에 대해서 연결요소 기법을 적용하여 각각의 독립된 한 개의 문자 영역으로 분할되어지고, 마지막으로 최소 거리 분류법에 의해 각각의 글자를 인식한다. 각각의 문자 인식을 위해 모양 기반 통계적 특징을 추출한다. 실험에서 제안된 전체적인 효율성 및 정확성을 분석하였으며, 현재 구현된 모바일 시스템의 실용성을 확인할 수 있었다. Text understand in natural images has become an active research field in the past few decades. In this paper, we present an automatic recognition system in Korean signboards with a complex background. The proposed algorithm includes detection, binarization and extraction of text for the recognition of shop names. First, we utilize an elaborate detection algorithm to detect possible text region based on edge histogram of vertical and horizontal direction. And detected text region is segmented by clustering method. Second, the text is divided into individual characters based on connected components whose conter of mass lie below the center line, which are recognized by using a minimum distance classifier. A shape-based statistical feature is adopted, which is adequate for Korean character recognition. The system has been implemented in a mobile phone and is demonstrated to show acceptable performance.
“이거 어디서 사?” - Mask R-CNN 기반 객체 분할을 활용한 패션 아이템 검색 시스템
정경희 ( Kyunghee Jung ),최하늘 ( Ha Nl Choi ),( Sammy Y. X. B. ),김현성 ( Hyunsung Kim ),( N. D. Toan ),추현승 ( Hyunseung Choo ) 한국정보처리학회 2022 한국정보처리학회 학술대회논문집 Vol.29 No.2
Mobile phones have become an essential item nowadays since it provides access to online platform and service fast and easy. Coming to these platforms such as Social Network Service (SNS) for shopping have been a go-to option for many people. However, searching for a specific fashion item in the picture is challenging, where users need to try multiple searches by combining appropriate search keywords. To tackle this problem, we propose a system that could provide immediate access to websites related to fashion items. In the framework, we also propose a deep learning model for an automatic analysis of image contexts using instance segmentation. We use transfer learning by utilizing Deep fashion 2 to maximize our model accuracy. After segmenting all the fashion item objects in the image, the related search information is retrieved when the object is clicked. Furthermore, we successfully deploy our system so that it could be assessable using any web browser. We prove that deep learning could be a promising tool not only for scientific purpose but also applicable to commercial shopping.